Point Cloud Planar Encoding Using Neighbor Node Occupancy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current point cloud encoding methods for planar structures are inefficient due to predictive encoding relying solely on prior reference information, leading to poor performance.
Innovation Solution
Perform predictive encoding and decoding of planar structure information based on occupancy information of neighboring nodes, considering the correlation between neighboring nodes to improve encoding and decoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If predictive encoding is performed on planar structure information using only prior reference information, then the encoding process is simple, but the encoding performance is poor
Solution Approach 1:
The patent performs preliminary classification of nodes into planar nodes and non-planar nodes before encoding. By pre-identifying planar nodes using occupancy information and structural criteria, the system prepares the data in advance for optimized encoding strategies, improving overall encoding performance without complicating the actual encoding process
Solution Approach 2:
The patent applies different encoding strategies to different types of nodes based on their local characteristics. Planar nodes are encoded using specialized planar encoding methods that exploit their geometric properties, while non-planar nodes use standard encoding. This localized approach improves encoding performance by matching the encoding method to the specific characteristics of each node type
2Productivity
If planar encoding is used for flat nodes to improve encoding efficiency, then the encoding efficiency improves, but the device complexity increases due to additional classification and processing requirements
Solution Approach 1:
The patent segments the point cloud data into planar nodes and non-planar nodes based on their geometric characteristics. By dividing the encoding process into separate handling for planar and non-planar nodes, the system can apply optimized encoding strategies to each segment, improving overall encoding efficiency while managing complexity through structured segmentation
Solution Approach 2:
The patent changes the encoding parameters and methods based on the node type. For planar nodes, it uses parameters and methods specifically optimized for planar geometry (such as encoding planar position information), while standard parameters are used for non-planar nodes. This parameter adaptation improves encoding efficiency for planar structures without requiring complete system redesign
Data Source
Figure 1A~3
Figure 4A
Figure 4B
AI summary
The present application provides a point cloud encoding/decoding method and apparatus, a device and a storage medium. The method comprises: when encoding/decoding plane structure information of a current node, determining N domain nodes of the current node; and encoding/decoding the plane structure information of the current node on the basis of placeholder information of the N domain nodes. That is to say, when predictive encoding/decoding is performed on the plane structure information of the current node, the correlation between the plane stricture information between adjacent nodes is considered, so that the geometric information encoding/decoding efficiency of a point cloud can be effectively improved, the predictive encoding/decoding performance of the plane structure information is further improved, and the encoding/decoding efficiency and performance of the point cloud are improved.